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AtacWorks: A deep convolutional neural network toolkit for epigenomics

View ORCID ProfileAvantika Lal, Zachary D. Chiang, Nikolai Yakovenko, Fabiana M. Duarte, Johnny Israeli, Jason D. Buenrostro
doi: https://doi.org/10.1101/829481
Avantika Lal
2NVIDIA Corporation, 2788 San Tomas Expy, Santa Clara, California 95051, USA
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  • ORCID record for Avantika Lal
Zachary D. Chiang
1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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Nikolai Yakovenko
2NVIDIA Corporation, 2788 San Tomas Expy, Santa Clara, California 95051, USA
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Fabiana M. Duarte
1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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Johnny Israeli
2NVIDIA Corporation, 2788 San Tomas Expy, Santa Clara, California 95051, USA
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  • For correspondence: jisraeli@nvidia.com jason_buenrostro@harvard.edu
Jason D. Buenrostro
1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA
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  • For correspondence: jisraeli@nvidia.com jason_buenrostro@harvard.edu
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Abstract

We introduce AtacWorks (https://github.com/clara-genomics/AtacWorks), a method to denoise and identify accessible chromatin regions from low-coverage or low-quality ATAC-seq data. AtacWorks uses a deep neural network to learn a mapping between noisy ATAC-seq data and corresponding higher-coverage or higher-quality data. To demonstrate the utility of AtacWorks, we train a model on data from four blood cell types and show that this model accurately denoises and identifies peaks from low-coverage bulk sequencing of different individuals, cell types, and experimental conditions. Further, we show that the deep learning model can be generalized to denoise low-quality data, aggregate single-cell ATAC-seq profiles, and Tn5 insertion sites for transcription factor footprinting. Finally, we apply our deep learning approach to denoise single-cell ATAC-seq data from hematopoietic stem cells to identify differentially-accessible regulatory elements between rare lineage-primed cell subpopulations.

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Posted November 04, 2019.
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AtacWorks: A deep convolutional neural network toolkit for epigenomics
Avantika Lal, Zachary D. Chiang, Nikolai Yakovenko, Fabiana M. Duarte, Johnny Israeli, Jason D. Buenrostro
bioRxiv 829481; doi: https://doi.org/10.1101/829481
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AtacWorks: A deep convolutional neural network toolkit for epigenomics
Avantika Lal, Zachary D. Chiang, Nikolai Yakovenko, Fabiana M. Duarte, Johnny Israeli, Jason D. Buenrostro
bioRxiv 829481; doi: https://doi.org/10.1101/829481

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